CN111300984A - Parameter self-tuning method for rotogravure printing system, rotogravure printing system - Google Patents

Abstract

The invention discloses a parameter self-tuning method and a rolling printing system for a rolling printing system. The method includes using a pseudo-random signal to perform closed-loop identification and low-frequency correction during the closed-loop operation of the rolling printing system, so as to obtain the low-frequency correction. Then use the model to obtain the disturbance distribution information and perform spectrum analysis on it to obtain the disturbance spectrum, and then adjust the controller parameters according to the disturbance spectrum and the model. The adjusted controller parameters meet the system's anti-interference ability and Stability requirements. The invention solves the problems of high performance requirements, large system characteristic changes and high order of the rolling printing system, so that the system can meet the requirements of stability, tracking and anti-interference under different rolling plate loads.

Description

Parameter self-tuning method for rotogravure printing system, rotogravure printing system

technical field

The invention relates to the technical field of servo control, in particular to a parameter self-tuning method for a rolling printing system and a rolling printing system.

Background technique

In the rolling printing press system, the motor and the load are generally connected through transmission mechanisms such as transmission shafts, gears or couplings. Due to the limited rigidity of the transmission mechanism and the large load inertia, there is a flexible transmission between the motor and the load, which makes the system exist. Mechanical resonance, the system often presents the characteristics of the third order or more. The rolling plate as a load often needs to be replaced, and the inertia, shape, and installation and connection of the rolling plate are different each time. System gain, mechanical resonance, etc. In this case, the original controller parameters may not meet the performance requirements of the new object, or even become unstable due to large changes in object characteristics. In this context, the self-tuning of the controller has become a problem that has to be solved in engineering practice.

At present, the commercial parameter self-tuning is divided into rule-based parameter self-tuning and model-based parameter self-tuning. The rule-based parameter self-tuning is difficult to formulate rules and unreasonable settings will lead to long convergence time or even unstable effects. Poor, the amount of calculation is large and difficult to achieve. The model-based parameter self-tuning depends on the accuracy of the mathematical model. The current model identification algorithms in commercial controllers are mostly frequency sweep identification and step identification. The model below the order is not suitable for the system with higher system order and with medium and high frequency resonance. For example, the latest series of drives from Mitsubishi have the functions of moment of inertia identification and parameter self-tuning. It first identifies the moment of inertia of the load through the acceleration and deceleration process, and then uses the empirical formula to tune the relevant parameters according to the moment of inertia of the load and the mechanical stiffness set by the user. This approach is not suitable for high-order systems such as rotogravure presses, which have mechanical resonance drift.

In addition, the conditions for applying the open-loop identification method are often not available in practical engineering applications. The process to be identified is under a closed-loop control environment, and the identified process is not allowed to open the closed-loop due to production safety and system reliability considerations. Therefore, when performing model-based parameter auto-tuning, closed-loop identification is particularly important.

SUMMARY OF THE INVENTION

The first object of the present invention is to overcome the shortcomings and deficiencies of the prior art, and to provide a parameter self-tuning method for a rolling printing system. The problem applies to roll printing systems.

The second object of the present invention is to provide a roll printing system, which can meet the requirements of stability, tracking and anti-interference under different rolling loads.

A third object of the present invention is to provide a computing device.

The first object of the present invention is achieved by the following technical solutions: a method for parameter self-tuning of a rolling printing system, comprising the following steps:

During the closed-loop operation of the rolling printing system, the pseudo-random signal is used for closed-loop identification and low-frequency correction, so as to obtain the high-order mathematical model after the low-frequency correction;

Obtain the disturbance distribution information by using the operating data and model of the system and perform spectrum analysis on it to obtain the disturbance spectrum;

The controller parameters are tuned according to the disturbance spectrum and model, and the tuned controller parameters meet the requirements of the anti-interference ability and stability of the system.

Preferably, a pseudo-random signal is used to perform closed-loop identification and low-frequency correction, so as to obtain a high-order mathematical model after low-frequency correction as follows:

(1) Use a conservative controller to keep the system in a closed-loop operation state, or use a gain self-adjusting controller to obtain a stable but conservative controller, so that the system can run stably to a steady state;

(2) A pseudo-random excitation signal of a fixed length is given to the controllable input point close to the object to be identified in the loop, so that the superimposed signal is transmitted to the input of the object to be identified, and then the input data and output of the object to be identified are collected according to the sampling period data until the pseudo-random signal excitation ends;

(3) Judging whether the upper limit of the frequency of the interference signal can be estimated, if not, data fusion is performed between the steady-state data and the input and output data of the object, and then the high-order model after the low-frequency correction is obtained by identification;

If so, obtain an identification model that can describe the characteristics of each frequency band of the system through the input and output data, and then perform low-frequency correction on the identification model to obtain a high-order mathematical model after the low-frequency correction.

Further, the object to be identified is the part of the system from the output end of the controller to the input end of the controller; the input and output data of the object to be identified refers to the input and output data ur ( _t ) of the object to be identified after superimposing the pseudo-random excitation signal. ), y _r (t).

Further, the steady-state data are the steady-state values u _sv and y _sv of the input and output of the object to be identified when the system runs to a steady state in step (1);

The steady-state data is fused with the input and output data of the object to be identified, and then the high-order model after low-frequency correction is obtained through identification, which is as follows:

Fuse u _sv and ur (t) to get u(t), and fuse _{y sv} and y _r (t) to get y(t), so that:

u(jw)| _w=0 =u _sv ;

u(jw)| _w≠0 = u _r (jw);

y(jw)| _w=0 = _ysv ;

y(jw)| _w≠0 =y _r (jw);

where _u (jw), ur (jw), y(jw), and y _r (jw) are the Fourier transforms of _{u(t), ur (t), y r (} t), and y(t), respectively frequency domain transform;

Then use the fusion obtained u(t), y(t) for identification.

Further, the acquisition of the identification model is specifically: according to the input and output data, using the least squares method or the subspace method to obtain the nth-order model transfer function G(s).

Further, low-frequency correction is performed on the identification model, as follows:

(1) Obtain the steady-state gain of the system according to the input and output data u _sv and y _sv when the object to be identified is steady-state

(2) Perform low-frequency separation on the transfer function G(s) of the n-order model to reduce the amount of calculation: G(s)=G _l (s)G _h (s), where G _l (s) is the low-frequency characteristic part of the system, G _h (s) is the high-frequency characteristic part of the system;

(3) Then solve the following formula:

G _l '(0)=K;

min|G _l '(jw)-G _l (jw)|,w>w _h ;

Among them, w is the frequency variable, G _l '(0)=G _l '(s)| _s=0 , G _l (jw)=G _l (s)| _s=jw , G _l '(jw)=G _l '(s)| _s=jw , refers to the frequency response of the model, w _h is the upper limit of the estimated low frequency interference frequency;

G _l '(s) is obtained from the above formula, thereby obtaining the corrected model G'(s)=G _l '(s)G _h (s).

Preferably, the controller parameters are tuned according to the disturbance spectrum and the model, and the process is as follows:

(1) Collect the running loop data in the stable operation phase, and obtain the disturbance estimation signal d(t) in the stationary phase according to the controller and the model G'(s);

(2) Transform the interference signal in the frequency domain, determine the frequency distribution and the main component of interference, and obtain the anti-interference ability index according to the performance index γ and the interference channel model S:

|S(jw)d(jw)|<γ;

Among them, S(jw) is the frequency response of the interference channel S, S(jw)=S(s)| _s=jw , d(jw) is the Fourier frequency domain transform of d(t);

(3) Use the loop forming method to solve the corresponding parameters of the controller, so that:

为相位裕度，G_m(·)为幅值裕度，

为期望幅值裕度，G_r为期望相位裕度。Among them, L(jw) is the system open-loop transfer function,

is the phase margin, G _m ( ) is the amplitude margin,

is the desired amplitude margin, and _Gr is the desired phase margin.

further more,

The second object of the present invention is achieved through the following technical solutions: a rolling printing system, the rolling printing system has a master station, a motor motion controller, a motor and an encoder, a coupling and a rolling plate connected in sequence, When the system is running in a closed loop, the parameters of the motor motion controller are designed through the parameter self-tuning method for the rolling printing system described in the first object of the present invention.

The third object of the present invention is achieved by the following technical solutions: a computing device, comprising a processor and a memory for storing a program executable by the processor, when the processor executes the program stored in the memory, the first object of the present invention is achieved The described parameter self-tuning method for the roll printing system.

Compared with the prior art, the present invention has the following advantages and effects:

(1) The present invention is directed to the parameter self-tuning method of the rotogravure printing system, including firstly, in the closed-loop operation process of the rotogravure printing system, using a pseudo-random signal to perform closed-loop identification and low-frequency correction, thereby obtaining a high-order mathematical model after the low-frequency correction ; Then use the operating data and model of the system to obtain the disturbance distribution information and perform spectrum analysis on it to obtain the disturbance spectrum; then adjust the controller parameters according to the disturbance spectrum and model, and the adjusted controller parameters satisfy the anti-interference ability and stability of the system. sexual requirements. The method of the invention is based on closed-loop identification of pseudo-random signals, low-frequency correction, and self-tuning of controller parameters based on models and disturbances, and can design a controller with higher performance and stronger stability, and solves the problem of high performance requirements of the rolling printing system and system problems. The problem of large variation in characteristics and high order is suitable for rolling printing systems.

(2) The method of the present invention inputs a pseudo-random signal as an excitation signal during closed-loop operation, which can stimulate the characteristics of each frequency band of the system, and then model by the input and output signals to obtain a high-order mathematical model that can describe the characteristics of each frequency band of the system.

(3) The method of the present invention utilizes the steady-state information in the running process and the model obtained by pseudo-random identification to perform information fusion, and optimizes and corrects the pseudo-random identification process and results, so that the low-frequency characteristics of the model are more accurate, and the accuracy of the model is further improved.

(4) The roll printing system of the present invention can realize the self-tuning of the controller parameters, and can meet the requirements of stability, tracking and anti-interference under different rolling loads, and the system has better performance and is worthy of promotion.

Description of drawings

FIG. 1 is an overall flow chart of a parameter self-tuning method for a roll printing system of the present invention.

FIG. 2 is a flowchart of closed-loop identification in the method of FIG. 1 .

FIG. 3 is a schematic structural diagram of the roll printing system of the present invention.

FIG. 4 is a printing flow chart of the system of FIG. 3 .

FIG. 5 is a time-domain fitting diagram of the closed-loop identification result and the measured output data.

FIG. 6 is a frequency domain fitting diagram of the closed-loop identification result and the measured output data.

Figure 7 is a comparison diagram of the given position of the motor tracking S-shaped given position curve and the measured position of the encoder after the controller is tuned.

FIG. 8 is a schematic diagram of the position tracking error of the motor in FIG. 7 tracking the S-shaped given position curve.

Detailed ways

The present invention will be described in further detail below with reference to the embodiments and the accompanying drawings, but the embodiments of the present invention are not limited thereto.

Example 1

This embodiment discloses a parameter self-tuning method for a rolling printing system, as shown in FIG. 1 , including the following steps:

S1. During the closed-loop operation of the stencil printing system, the pseudo-random signal is used for closed-loop identification and low-frequency correction, so as to obtain a high-order mathematical model after low-frequency correction, as follows:

(1) As shown in Figure 2, use a conservative controller to keep the system in a closed-loop operation state, or use a gain self-adjusting controller to obtain a stable but conservative controller, so that the system runs stably to a steady state.

(2) A pseudo-random excitation signal of a fixed length is given to the controllable input point close to the object to be identified in the loop, so that the superimposed signal is transmitted to the input of the object to be identified, and then the input data and output of the object to be identified are collected according to the sampling period data until the pseudo-random signal excitation ends.

Compared with the step signal and the sine frequency sweep signal, the pseudo-random signal has a relatively uniform power distribution in each frequency band in the sampling frequency band, and the length is short. It is an approximate white noise signal with a constant power spectral density. Input pseudo-random signal as the excitation signal can stimulate the characteristics of each frequency band of the system.

Under the requirement of controller parameter self-tuning, the object to be identified is the part of the system from the controller output end to the controller input end. In this example, it refers to the part of the system between the drive input end of the rolling printing system and the speed output terminal of the encoder, including the rolling printing system driver, the rolling printing system motor, the motor load, the encoder and the intermediate connection link (connection link). shaft) these parts.

The input and output data of the object to be identified refers to the input and output data ur (t) and y _r (t ₎ of the object to be identified after superimposing pseudo-random excitation.

(3) Since the power distribution of the pseudo-random signal in each frequency band in the sampling frequency band is relatively uniform, but the motor system is often affected by low-frequency interference during the operation process, so the low-frequency part of the model often has a certain deviation from the actual during the pseudo-random identification. Determine whether the upper limit of the frequency of the interference signal can be estimated.

Under normal circumstances, for the rolling printing system, the main frequency of system interference can be roughly judged through mechanism analysis; or the position tracking error of a section of printing press system can be collected in a stable operation state, and Fourier spectrum analysis is performed on it to determine the main frequency of system interference. frequency band.

If not, the steady-state data, that is, the steady-state values u _sv and y _sv of the input and output of the object to be identified when the system runs to a steady state in step (1), and the input and output data of the object to be identified ur ( _t ), y _r (t) performs data fusion, and then identifies the high-order model after low-frequency correction:

Fuse u _sv and ur (t) to get u(t), and fuse _{y sv} and y _r (t) to get y(t), so that:

u(jw)| _w=0 =u _sv ;

u(jw)| _w≠0 = u _r (jw);

y(jw)| _w=0 = _ysv ;

y(jw)| _w≠0 =y _r (jw);

where _u (jw), ur (jw), y(jw), and y _r (jw) are the Fourier transforms of _{u(t), ur (t), y r (} t), and y(t), respectively frequency domain transform;

Then use the fusion obtained u(t), y(t) for identification.

If so, obtain an identification model that can describe the characteristics of each frequency band of the system through the input and output data, and then perform low-frequency correction on the identification model to obtain a high-order mathematical model after the low-frequency correction.

In this embodiment, according to the input and output data, the least square method or the subspace method can be used to obtain the nth-order model transfer function G(s), that is, the identification model.

Perform low-frequency correction on the identification model, as follows:

(1) Obtain the steady-state gain of the system according to the input and output data u _sv and y _sv when the object to be identified is steady-state

(2) Perform low-frequency separation on the transfer function G(s) of the n-order model to reduce the amount of calculation: G(s)=G _l (s)G _h (s), where G _l (s) is the low-frequency characteristic part of the system, G _h (s) is the high-frequency characteristic part of the system;

(3) Then solve the following formula to obtain the corrected low-frequency characteristic part model G _l '(s) of the system:

指的是模型的频率响应，w_h为预估低频干扰频率上限；where w is the frequency variable,

refers to the frequency response of the model, and w _h is the upper limit of the estimated low frequency interference frequency;

The above formula can be solved in various ways, for example, it can be solved by the least square method. After solving G _l '(s), the corrected model G'(s)=G _l '(s)G _h (s) can be obtained.

S2. Obtain the disturbance distribution information (interference signal) by using the operating data and model of the system, and perform spectrum analysis on it to obtain the disturbance spectrum;

S3. Set the controller parameters according to the disturbance spectrum and model. The adjusted controller parameters meet the anti-interference ability and stability requirements of the system. The process is as follows:

(1) Collect the running loop data in the stationary running stage, and obtain the disturbance estimation signal d(t) in the stationary stage according to the controller and the model G'(s).

(2) Transform the interference signal in the frequency domain, determine the frequency distribution and the main component of interference, and obtain the anti-interference ability index according to the performance index γ and the interference channel model S:

|S(jw)d(jw)|<γ;

Among them, S(jw) is the frequency response of the interference channel S, S(jw)=S(s)| _s=jw , d(jw) is the Fourier frequency domain transform of d(t).

(3) Use the loop forming method to solve the corresponding parameters of the controller, so that:

为相位裕度，G_m(·)为幅值裕度，

为期望幅值裕度，G_r为期望相位裕度。期望裕度根据作业要求而定，一般可设为

满足以上约束的控制器参数即能使得系统的稳定性和跟踪性能达标。Among them, L(jw) is the system open-loop transfer function,

is the phase margin, G _m ( ) is the amplitude margin,

is the desired amplitude margin, and _Gr is the desired phase margin. The expected margin depends on the job requirements, and can generally be set as

The controller parameters satisfying the above constraints can make the system stability and tracking performance meet the standards.

As shown in FIG. 3 , the rolling printing system of this embodiment has a main station, a motor motion controller, a motor and an encoder, a coupling and a rolling plate connected in sequence, and the main station sends the motion command to the motor for motion through the bus. The controller, the electrode motion controller sends control signals to the motor and the encoder, the encoder feeds back the rolling running status to the motor motion controller in real time, and the motor motion controller uploads the running status to the master station.

Figure 4 shows the printing process of the system: (1) Before starting printing, each component is powered off, the roll plate is replaced, the material is loaded, and the power is turned on; (2) The motor runs stably at low speed, and the motion controllers of each motor are in synchronization. state, using the above parameter self-tuning method, design the controller parameters that meet the requirements of anti-interference ability and stability during high-speed operation, mixing, this stage has no performance requirements, and lasts for 30 minutes; (3) The high-speed operation of each motor, Start printing, the performance requirements of this stage are high.

Figure 5 is a time-domain fitting diagram of the closed-loop identification result and the measured output data. The measured input and output data of the object to be identified are collected, the input data is used as the input of the identification model, the output of the identification model is calculated, and the model is output (dotted line) Compared with the measured output (solid line) in the time domain. Figure 6 is the frequency domain fitting diagram of the closed-loop identification result and the measured output data. The measured input and output data of the object to be identified are used for spectrum analysis, and the spectral division result (solid line) is compared with the identification result (dotted line) in the Bode plot. As shown in Figure 5 and Figure 6, the solid line and the dashed line are roughly consistent, indicating that the model is accurate and can be applied to a system with high system order and large load changes, such as a rotogravure printing system.

Figure 7 is a comparison diagram of the given position (dotted line) and the encoder's measured position (solid line) after the motor of the printing system tracks the S-shaped given position curve after tuning the controller. It can be seen from Figure 7 that the two curves overlap, Figure 8 For the position tracking error of the corresponding printing system motor tracking the S-shaped given position curve, it can be obtained from Figure 8. The measured error is generally within plus or minus 2 filaments in a steady state, and the system requires that the error should not exceed plus or minus 10 filaments. It can be seen that, The system of this embodiment can meet the performance requirements.

Example 2

This embodiment discloses a computing device, including a processor and a memory for storing a program executable by the processor. When the processor executes a program stored in the memory, the processor implements the parameters for the roll printing system described in Embodiment 1 The self-tuning method is as follows:

During the closed-loop operation of the rolling printing system, the pseudo-random signal is used for closed-loop identification and low-frequency correction, so as to obtain the high-order mathematical model after the low-frequency correction;

Obtain the disturbance distribution information by using the operating data and model of the system and perform spectrum analysis on it to obtain the disturbance spectrum;

The controller parameters are tuned according to the disturbance spectrum and model, and the tuned controller parameters meet the requirements of the anti-interference ability and stability of the system.

The computing device described in this embodiment may be a desktop computer, a notebook computer, a smart phone, a PDA handheld terminal, a tablet computer, or other terminal device having a processor function.

The above-mentioned embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited by the above-mentioned embodiments, and any other changes, modifications, substitutions, combinations, The simplification should be equivalent replacement manners, which are all included in the protection scope of the present invention.

Claims (10)

1. a kind of parameter self-tuning method for rotogravure printing system, is characterized in that, comprises the steps: During the closed-loop operation of the rolling printing system, the pseudo-random signal is used for closed-loop identification and low-frequency correction, so as to obtain the high-order mathematical model after the low-frequency correction; Obtain the disturbance distribution information by using the operating data and model of the system and perform spectrum analysis on it to obtain the disturbance spectrum; The controller parameters are tuned according to the disturbance spectrum and model, and the tuned controller parameters meet the requirements of the anti-interference ability and stability of the system. 2. the parameter self-tuning method for stencil printing system according to claim 1, is characterized in that, uses pseudo-random signal to carry out closed-loop identification and low-frequency correction, thereby the process of obtaining the higher-order mathematical model after low-frequency correction is as follows: (1) Use a conservative controller to keep the system in a closed-loop operation state, or use a gain self-adjusting controller to obtain a stable but conservative controller, so that the system can run stably to a steady state; (2) A pseudo-random excitation signal of a fixed length is given to the controllable input point close to the object to be identified in the loop, so that the superimposed signal is transmitted to the input of the object to be identified, and then the input data and output of the object to be identified are collected according to the sampling period data until the pseudo-random signal excitation ends; (3) judging whether the upper limit of the frequency of the interference signal can be estimated, if not, data fusion is performed between the steady-state data and the input and output data of the object to be identified, and then the high-order model after the low-frequency correction is obtained by identification; If so, obtain an identification model that can describe the characteristics of each frequency band of the system through the input and output data, and then perform low-frequency correction on the identification model to obtain a high-order mathematical model after the low-frequency correction. 3. The parameter self-tuning method for a roll printing system according to claim 2, wherein the object to be identified is the part of the system from the controller output end to the controller input end; the input and output data of the object to be identified refers to the superposition of After the pseudo-random excitation signal, the input and output data ur (t) and y _r (t ₎ of the object to be identified. 4. the parameter self-tuning method for the roll printing system according to claim 3, is characterized in that, the steady state data is the steady state value u _sv of the input and output of the object to be identified when the system runs to steady state in step (1). and y _sv ; The steady-state data is fused with the input and output data of the object to be identified, and then the high-order model after low-frequency correction is obtained through identification, which is as follows: Fuse u _sv and ur (t) to get u(t), and fuse _{y sv} and y _r (t) to get y(t), so that: u(jw)| _w=0 =u _sv ; u(jw)| _w≠0 = u _r (jw); y(jw)| _w=0 = _ysv ; y(jw)| _w≠0 =y _r (jw); where _u (jw), ur (jw), y(jw), and y _r (jw) are the Fourier transforms of _{u(t), ur (t), y r (} t), and y(t), respectively frequency domain transform; Then use the fusion obtained u(t), y(t) for identification. 5. the parameter self-tuning method for the roll printing system according to claim 2 is characterized in that, the acquisition of identification model is specifically: according to input and output data, use least squares method or subspace method to obtain n-order model transfer function G(s). 6. The parameter self-tuning method for the roll printing system according to claim 5, is characterized in that, the identification model is carried out low-frequency correction, as follows: (1) Obtain the steady-state gain of the system according to the input and output data u _sv and y _sv when the object to be identified is steady-state

(2) Perform low-frequency separation on the transfer function G(s) of the n-order model to reduce the amount of calculation: G(s)=G _l (s)G _h (s), where G _l (s) is the low-frequency characteristic part of the system, G _h (s) is the high-frequency characteristic part of the system; (3) Then solve the following formula: G _l '(0)=K; min|G _l '(jw)-G _l (jw)|,w>w _h ; Among them, w is the frequency variable, G _l '(0)=G _l '(s)| _s=0 , G _l (jw)=G _l (s)| _s=jw , G _l '(jw)=G _l '(s)| _s=jw , refers to the frequency response of the model, w _h is the upper limit of the estimated low frequency interference frequency; G _l '(s) is obtained from the above formula, thereby obtaining the corrected model G'(s)=G _l '(s)G _h (s). 7. the parameter self-tuning method for stencil printing system according to claim 1, is characterized in that, according to disturbance spectrum and model, carry out controller parameter setting, and process is as follows: (1) Collect the running loop data in the stable operation phase, and obtain the disturbance estimation signal d(t) in the stationary phase according to the controller and the model G'(s); (2) Transform the interference signal in the frequency domain, determine the frequency distribution and the main component of interference, and obtain the anti-interference ability index according to the performance index γ and the interference channel model S: |S(jw)d(jw)|<γ; Among them, S(jw) is the frequency response of the interference channel S, S(jw)=S(s)| _s=jw , d(jw) is the Fourier frequency domain transform of d(t); (3) Use the loop forming method to solve the corresponding parameters of the controller, so that: |S(jw)d(jw)|<γ;

|G _m (L(jw))>G _r |; Among them, L(jw) is the system open-loop transfer function,

is the phase margin, G _m ( ) is the amplitude margin,

is the desired amplitude margin, and _Gr is the desired phase margin. 8. The parameter self-tuning method for a roll printing system according to claim 7, characterized in that,

_Gr = 5db. 9. A rotogravure printing system, characterized in that the rotogravure printing system has a master station, a motor motion controller, a motor and an encoder, a coupling and a rotoplate connected in sequence, and when the system is in closed-loop operation, the The parameters of the motor motion controller are designed for the parameter self-tuning method of the roll printing system according to any one of claims 1 to 8. 10. A computing device, comprising a processor and a memory for storing a program executable by the processor, wherein when the processor executes the program stored in the memory, the processor described in any one of claims 1 to 8 is implemented. Parameter self-tuning method for rotogravure printing system.

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